4.7 Article

Establishment and validation of a novel prognostic model for non-virus-related hepatocellular carcinoma

期刊

CANCER CELL INTERNATIONAL
卷 22, 期 1, 页码 -

出版社

BMC
DOI: 10.1186/s12935-022-02725-5

关键词

NV-HCC; Prognosis model; LASSO-Cox regression

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资金

  1. Guangdong Esophageal Cancer Institute Science and Technology Program [Q202001]
  2. Nursery Fund of Henan Cancer Hospital

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This study aimed to establish a prognostic model to evaluate the overall survival (OS) of patients with non-virus-related hepatocellular carcinoma (NV-HCC). The prognostic model was developed using LASSO-Cox regression analysis and its predictive ability was evaluated using the concordance index (C-index) and time-dependent receiver operating characteristic curve (TD-ROC). The results showed that the prognostic model had better predictive ability than TNM stage and treatment, and could assist clinicians in designing individualized therapeutic strategies for NV-HCC patients.
Objective The incidence of non-virus-related hepatocellular carcinoma (NV-HCC) in hepatocellular carcinoma (HCC) is steadily increasing. The aim of this study was to establish a prognostic model to evaluate the overall survival (OS) of NV-HCC patients. Methods Overall, 261 patients with NV-HCC were enrolled in this study. A prognostic model was developed by using LASSO-Cox regression analysis. The prognostic power was appraised by the concordance index (C-index), and the time-dependent receiver operating characteristic curve (TD-ROC). Kaplan-Meier (K-M) survival analysis was used to evaluate the predictive ability in the respective subgroups stratified by the prognostic model risk score. A nomogram for survival prediction was established by integrating the prognostic model, TNM stage, and treatment. Results According to the LASSO-Cox regression results, the number of nodules, lymphocyte-to-monocyte ratio (LMR), prognostic nutritional index (PNI), alkaline phosphatase (ALP), aspartate aminotransferase (AST)/alanine aminotransferase (ALT) ratio (SLR) and C-reactive protein (CRP) were included for prognostic model construction. The C-index of the prognostic model was 0.759 (95% CI 0.723-0.797) in the development cohort and 0.796 (95% CI 0.737-0.855) in the validation cohort, and its predictive ability was better than TNM stage and treatment. The TD-ROC showed similar results. K-M survival analysis showed that NV-HCC patients with low risk scores had a better prognosis (P < 0.05). A nomogram based on the prognostic model, TNM stage, and treatment was constructed with sufficient discriminatory power with C-indexes of 0.78 and 0.85 in the development and validation cohort, respectively. Conclusion For NV-HCC, this prognostic model could predict an OS benefit for patients, which may assist clinicians in designing individualized therapeutic strategies.

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